Abstract

Inhibitory neurons play a critical role in decision-making models and are often simplified as a single pool of non-selective neurons lacking connection specificity. This assumption is in keeping with observations in primary visual cortex: inhibitory neurons are broadly tuned in vivo, and show non-specific connectivity in slice. Selectivity of excitatory and inhibitory neurons within decision circuits is not known. We simultaneously measured their activity in the posterior parietal cortex of mice making multisensory decisions. Surprisingly, excitatory and inhibitory neurons were equally selective for the animals choice, both at the single cell and population level. Further, excitatory and inhibitory populations exhibited similar changes in selectivity and temporal dynamics during the transition from novice to expert decision-making, paralleling behavioral improvements. These observations, combined with simulations, argue against models assuming non-selective inhibitory neurons. Instead, they argue for selective subnetworks of inhibitory and excitatory neurons that are shaped by experience to support expert decision-making.

Footnotes

We complemented our experimental findings with modeling work. We compared decision-making models with non-selective vs. selective inhibition. In agreement with our experimental findings, we found that in non-selective inhibition networks, inhibitory neurons will always have lower choice decoding accuracy compared to excitatory neurons, regardless of the model parameters. However, if the connectivity between excitatory and inhibitory neurons is selective, either based on signal preference or signal/ noise ratio, inhibitory neurons can predict the animal's choice as accurately as excitatory neurons. Altogether our experimental and theoretical findings provide strong evidence in favor of selective connectivity between excitatory and inhibitory neurons in decision circuits.

Copyright

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